class_docstr: TCN
  
  Temporal Convolution Network (TCN), with MLP decoder.
  The historical encoder uses dilated skip connections to obtain efficient long memory,
  while the rest of the architecture allows for future exogenous alignment.
  
  **Parameters:**<br>
  `h`: int, forecast horizon.<br>
  `input_size`: int, maximum sequence length for truncated train backpropagation. Default -1 uses all history.<br>
  `inference_input_size`: int, maximum sequence length for truncated inference. Default -1 uses all history.<br>
  `kernel_size`: int, size of the convolving kernel.<br>
  `dilations`: int list, ontrols the temporal spacing between the kernel points; also known as the à trous algorithm.<br>
  `encoder_hidden_size`: int=200, units for the TCN's hidden state size.<br>
  `encoder_activation`: str=`tanh`, type of TCN activation from `tanh` or `relu`.<br>
  `context_size`: int=10, size of context vector for each timestamp on the forecasting window.<br>
  `decoder_hidden_size`: int=200, size of hidden layer for the MLP decoder.<br>
  `decoder_layers`: int=2, number of layers for the MLP decoder.<br>
  `futr_exog_list`: str list, future exogenous columns.<br>
  `hist_exog_list`: str list, historic exogenous columns.<br>
  `stat_exog_list`: str list, static exogenous columns.<br>
  `loss`: PyTorch module, instantiated train loss class from [losses collection](https://nixtla.github.io/neuralforecast/losses.pytorch.html).<br>
  `max_steps`: int=1000, maximum number of training steps.<br>
  `learning_rate`: float=1e-3, Learning rate between (0, 1).<br>
  `valid_batch_size`: int=None, number of different series in each validation and test batch.<br>
  `num_lr_decays`: int=-1, Number of learning rate decays, evenly distributed across max_steps.<br>
  `early_stop_patience_steps`: int=-1, Number of validation iterations before early stopping.<br>
  `val_check_steps`: int=100, Number of training steps between every validation loss check.<br>    `batch_size`: int=32, number of differentseries in each batch.<br>
  `scaler_type`: str='robust', type of scaler for temporal inputs normalization see [temporal scalers](https://nixtla.github.io/neuralforecast/common.scalers.html).<br>
  `random_seed`: int=1, random_seed for pytorch initializer and numpy generators.<br>
  `num_workers_loader`: int=os.cpu_count(), workers to be used by `TimeSeriesDataLoader`.<br>
  `drop_last_loader`: bool=False, if True `TimeSeriesDataLoader` drops last non-full batch.<br>
  `alias`: str, optional,  Custom name of the model.<br>
  `**trainer_kwargs`: int,  keyword trainer arguments inherited from [PyTorch Lighning's trainer](https://pytorch-lightning.readthedocs.io/en/stable/api/pytorch_lightning.trainer.trainer.Trainer.html?highlight=trainer).<br>
code: def __init__(
      self,
      h: int,
      input_size: int = -1,
      inference_input_size: int = -1,
      kernel_size: int = 2,
      dilations: List[int] = [1, 2, 4, 8, 16],
      encoder_hidden_size: int = 200,
      encoder_activation: str = "ReLU",
      context_size: int = 10,
      decoder_hidden_size: int = 200,
      decoder_layers: int = 2,
      futr_exog_list=None,
      hist_exog_list=None,
      stat_exog_list=None,
      loss=MAE(),
      valid_loss=None,
      max_steps: int = 1000,
      learning_rate: float = 1e-3,
      num_lr_decays: int = -1,
      early_stop_patience_steps: int = -1,
      val_check_steps: int = 100,
      batch_size: int = 32,
      valid_batch_size: Optional[int] = None,
      scaler_type: str = "robust",
      random_seed: int = 1,
      num_workers_loader=0,
      drop_last_loader=False,
      **trainer_kwargs,
  ):
      super(TCN, self).__init__(
          h=h,
          input_size=input_size,
          inference_input_size=inference_input_size,
          loss=loss,
          valid_loss=valid_loss,
          max_steps=max_steps,
          learning_rate=learning_rate,
          num_lr_decays=num_lr_decays,
          early_stop_patience_steps=early_stop_patience_steps,
          val_check_steps=val_check_steps,
          batch_size=batch_size,
          valid_batch_size=valid_batch_size,
          scaler_type=scaler_type,
          futr_exog_list=futr_exog_list,
          hist_exog_list=hist_exog_list,
          stat_exog_list=stat_exog_list,
          num_workers_loader=num_workers_loader,
          drop_last_loader=drop_last_loader,
          random_seed=random_seed,
          **trainer_kwargs,
      )
  
      # ----------------------------------- Parse dimensions -----------------------------------#
      # TCN
      self.kernel_size = kernel_size
      self.dilations = dilations
      self.encoder_hidden_size = encoder_hidden_size
      self.encoder_activation = encoder_activation
  
      # Context adapter
      self.context_size = context_size
  
      # MLP decoder
      self.decoder_hidden_size = decoder_hidden_size
      self.decoder_layers = decoder_layers
  
      self.futr_exog_size = len(self.futr_exog_list)
      self.hist_exog_size = len(self.hist_exog_list)
      self.stat_exog_size = len(self.stat_exog_list)
  
      # TCN input size (1 for target variable y)
      input_encoder = 1 + self.hist_exog_size + self.stat_exog_size
  
      # ---------------------------------- Instantiate Model -----------------------------------#
      # Instantiate historic encoder
      self.hist_encoder = TemporalConvolutionEncoder(
          in_channels=input_encoder,
          out_channels=self.encoder_hidden_size,
          kernel_size=self.kernel_size,  # Almost like lags
          dilations=self.dilations,
          activation=self.encoder_activation,
      )
  
      # Context adapter
      self.context_adapter = nn.Linear(
          in_features=self.encoder_hidden_size
          + self.futr_exog_size * h,
          out_features=self.context_size * h,
      )
  
      # Decoder MLP
      self.mlp_decoder = MLP(
          in_features=self.context_size + self.futr_exog_size,
          out_features=self.loss.outputsize_multiplier,
          hidden_size=self.decoder_hidden_size,
          num_layers=self.decoder_layers,
          activation="ReLU",
          dropout=0.0,
      )
  
file_path: neuralforecast/neuralforecast/models/tcn.py
imports: from typing import List, Optional
  import torch
  import torch.nn
  from ..losses.pytorch import MAE
  from ..common._base_recurrent import BaseRecurrent
  from ..common._modules import MLP, TemporalConvolutionEncoder
label_desc: Use image node_id 1 to create a new TCN object from inherited base classes: BaseRecurrent with example: obj = TCN(h, input_size, inference_input_size, kernel_size, dilations, encoder_hidden_size, encoder_activation, context_size, decoder_hidden_size, decoder_layers, futr_exog_list, hist_exog_list, stat_exog_list, loss, valid_loss, max_steps, learning_rate, num_lr_decays, early_stop_patience_steps, val_check_steps, batch_size, valid_batch_size, scaler_type, random_seed, num_workers_loader, drop_last_loader)